{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据集Dataloader制作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 如何自定义数据集：\n",
    "\n",
    "- 1.数据和标签的目录结构先搞定(得知道到哪读数据)\n",
    "- 2.写好读取数据和标签路径的函数(根据自己数据集情况来写)\n",
    "- 3.完成单个数据与标签读取函数(给dataloader举一个例子)\n",
    "\n",
    "### 咱们以花朵数据集为例：\n",
    "\n",
    "- 原来数据集都是以文件夹为类别ID，现在咱们换一个套路，用txt文件指定数据路径与标签(实际情况基本都这样)\n",
    "- 这回咱们的任务就是在txt文件中获取图像路径与标签，然后把他们交给dataloader\n",
    "- 核心代码非常简单，按照对应格式传递需要的数据和标签就可以啦"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "colab_type": "code",
    "id": "Qqk2lCKzYf-y",
    "outputId": "2354909a-d734-4dd8-e887-eb5373d9127e",
    "ExecuteTime": {
     "end_time": "2024-04-11T16:07:34.656499800Z",
     "start_time": "2024-04-11T16:07:34.548691100Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch import nn\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "#pip install torchvision\n",
    "from torchvision import transforms, models, datasets\n",
    "#https://pytorch.org/docs/stable/torchvision/index.html\n",
    "import imageio\n",
    "import time\n",
    "import warnings\n",
    "import random\n",
    "import sys\n",
    "import copy\n",
    "import json\n",
    "from PIL import Image\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 先来分细节整明白咱一会要干啥！\n",
    "\n",
    "#### 任务1：读取txt文件中的路径和标签\n",
    "\n",
    "- 第一个小任务，从标注文件中读取数据和标签\n",
    "- 至于你准备存成什么格式，都可以的，一会能取出来东西就行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-04-11T16:07:34.666500300Z",
     "start_time": "2024-04-11T16:07:34.552891800Z"
    }
   },
   "outputs": [],
   "source": [
    "def load_annotations(ann_file):\n",
    "    data_infos = {}\n",
    "    with open(ann_file) as f:\n",
    "        samples = [x.strip().split(' ') for x in f.readlines()]\n",
    "        for filename, gt_label in samples:\n",
    "            data_infos[filename] = np.array(gt_label, dtype=np.int64)\n",
    "    return data_infos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T16:07:34.718149300Z",
     "start_time": "2024-04-11T16:07:34.559493100Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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dtype=int64), 'image_07521.jpg': array(94, dtype=int64), 'image_07522.jpg': array(94, dtype=int64), 'image_07523.jpg': array(94, dtype=int64), 'image_07524.jpg': array(94, dtype=int64), 'image_07525.jpg': array(94, dtype=int64), 'image_07526.jpg': array(94, dtype=int64), 'image_07527.jpg': array(94, dtype=int64), 'image_07528.jpg': array(94, dtype=int64), 'image_07529.jpg': array(94, dtype=int64), 'image_07530.jpg': array(94, dtype=int64), 'image_07531.jpg': array(94, dtype=int64), 'image_07532.jpg': array(94, dtype=int64), 'image_07533.jpg': array(94, dtype=int64), 'image_07534.jpg': array(94, dtype=int64), 'image_07535.jpg': array(94, dtype=int64), 'image_07537.jpg': array(94, dtype=int64), 'image_07538.jpg': array(94, dtype=int64), 'image_07539.jpg': array(94, dtype=int64), 'image_07540.jpg': array(94, dtype=int64), 'image_07541.jpg': array(94, dtype=int64), 'image_07542.jpg': array(94, dtype=int64), 'image_07543.jpg': array(94, dtype=int64), 'image_07544.jpg': array(94, dtype=int64), 'image_07545.jpg': array(94, dtype=int64), 'image_07546.jpg': array(94, dtype=int64), 'image_07547.jpg': array(94, dtype=int64), 'image_07548.jpg': array(94, dtype=int64), 'image_07549.jpg': array(94, dtype=int64), 'image_07552.jpg': array(94, dtype=int64), 'image_07553.jpg': array(94, dtype=int64), 'image_07554.jpg': array(94, dtype=int64), 'image_07555.jpg': array(94, dtype=int64), 'image_07556.jpg': array(94, dtype=int64), 'image_07557.jpg': array(94, dtype=int64), 'image_07558.jpg': array(94, dtype=int64), 'image_07559.jpg': array(94, dtype=int64), 'image_07560.jpg': array(94, dtype=int64), 'image_07562.jpg': array(94, dtype=int64), 'image_07563.jpg': array(94, dtype=int64), 'image_07564.jpg': array(94, dtype=int64), 'image_07565.jpg': array(94, dtype=int64), 'image_07566.jpg': array(94, dtype=int64), 'image_07567.jpg': array(94, dtype=int64), 'image_07568.jpg': array(94, dtype=int64), 'image_07569.jpg': array(94, dtype=int64), 'image_07570.jpg': array(94, dtype=int64), 'image_07571.jpg': array(94, dtype=int64), 'image_07572.jpg': array(94, dtype=int64), 'image_07574.jpg': array(94, dtype=int64), 'image_07576.jpg': array(94, dtype=int64), 'image_07577.jpg': array(94, dtype=int64), 'image_07578.jpg': array(94, dtype=int64), 'image_07579.jpg': array(94, dtype=int64), 'image_07582.jpg': array(94, dtype=int64), 'image_07583.jpg': array(94, dtype=int64), 'image_07586.jpg': array(94, dtype=int64), 'image_07587.jpg': array(94, dtype=int64), 'image_07589.jpg': array(94, dtype=int64), 'image_07590.jpg': array(94, dtype=int64), 'image_07591.jpg': array(94, dtype=int64), 'image_07592.jpg': array(94, dtype=int64), 'image_07593.jpg': array(94, dtype=int64), 'image_07594.jpg': array(95, dtype=int64), 'image_07595.jpg': array(95, dtype=int64), 'image_07598.jpg': array(95, dtype=int64), 'image_07599.jpg': array(95, dtype=int64), 'image_07600.jpg': array(95, dtype=int64), 'image_07601.jpg': array(95, dtype=int64), 'image_07602.jpg': array(95, dtype=int64), 'image_07603.jpg': array(95, dtype=int64), 'image_07604.jpg': array(95, dtype=int64), 'image_07607.jpg': array(95, dtype=int64), 'image_07608.jpg': array(95, dtype=int64), 'image_07610.jpg': array(95, dtype=int64), 'image_07611.jpg': array(95, dtype=int64), 'image_07612.jpg': array(95, dtype=int64), 'image_07614.jpg': array(95, dtype=int64), 'image_07615.jpg': array(95, dtype=int64), 'image_07616.jpg': array(95, dtype=int64), 'image_07617.jpg': array(95, dtype=int64), 'image_07618.jpg': array(95, dtype=int64), 'image_07619.jpg': array(95, dtype=int64), 'image_07620.jpg': array(95, dtype=int64), 'image_07621.jpg': array(95, dtype=int64), 'image_07623.jpg': array(95, dtype=int64), 'image_07624.jpg': array(95, dtype=int64), 'image_07625.jpg': array(95, dtype=int64), 'image_07626.jpg': array(95, dtype=int64), 'image_07627.jpg': array(95, dtype=int64), 'image_07628.jpg': array(95, dtype=int64), 'image_07629.jpg': array(95, dtype=int64), 'image_07630.jpg': array(95, dtype=int64), 'image_07631.jpg': array(95, dtype=int64), 'image_07632.jpg': array(95, dtype=int64), 'image_07633.jpg': array(95, dtype=int64), 'image_07634.jpg': array(95, dtype=int64), 'image_07635.jpg': array(95, dtype=int64), 'image_07636.jpg': array(95, dtype=int64), 'image_07637.jpg': array(95, dtype=int64), 'image_07638.jpg': array(95, dtype=int64), 'image_07639.jpg': array(95, dtype=int64), 'image_07640.jpg': array(95, dtype=int64), 'image_07641.jpg': array(95, dtype=int64), 'image_07642.jpg': array(95, dtype=int64), 'image_07643.jpg': array(95, dtype=int64), 'image_07644.jpg': array(95, dtype=int64), 'image_07645.jpg': array(95, dtype=int64), 'image_07647.jpg': array(95, dtype=int64), 'image_07648.jpg': array(95, dtype=int64), 'image_07649.jpg': array(95, dtype=int64), 'image_07650.jpg': array(95, dtype=int64), 'image_07651.jpg': array(95, dtype=int64), 'image_07652.jpg': array(95, dtype=int64), 'image_07654.jpg': array(95, dtype=int64), 'image_07655.jpg': array(95, dtype=int64), 'image_07657.jpg': array(95, dtype=int64), 'image_07659.jpg': array(95, dtype=int64), 'image_07660.jpg': array(95, dtype=int64), 'image_07661.jpg': array(95, dtype=int64), 'image_07663.jpg': array(95, dtype=int64), 'image_07664.jpg': array(95, dtype=int64), 'image_07665.jpg': array(95, dtype=int64), 'image_07666.jpg': array(95, dtype=int64), 'image_07667.jpg': array(95, dtype=int64), 'image_07671.jpg': array(95, dtype=int64), 'image_07673.jpg': array(95, dtype=int64), 'image_07674.jpg': array(95, dtype=int64), 'image_07675.jpg': array(95, dtype=int64), 'image_07678.jpg': array(95, dtype=int64), 'image_07679.jpg': array(95, dtype=int64), 'image_07680.jpg': array(95, dtype=int64), 'image_07681.jpg': array(95, dtype=int64), 'image_07682.jpg': array(95, dtype=int64), 'image_07684.jpg': array(95, dtype=int64), 'image_07685.jpg': array(96, dtype=int64), 'image_07686.jpg': array(96, dtype=int64), 'image_07687.jpg': array(96, dtype=int64), 'image_07688.jpg': array(96, dtype=int64), 'image_07689.jpg': array(96, dtype=int64), 'image_07690.jpg': array(96, dtype=int64), 'image_07691.jpg': array(96, dtype=int64), 'image_07692.jpg': array(96, dtype=int64), 'image_07693.jpg': array(96, dtype=int64), 'image_07694.jpg': array(96, dtype=int64), 'image_07695.jpg': array(96, dtype=int64), 'image_07698.jpg': array(96, dtype=int64), 'image_07700.jpg': array(96, dtype=int64), 'image_07701.jpg': array(96, dtype=int64), 'image_07702.jpg': array(96, dtype=int64), 'image_07703.jpg': array(96, dtype=int64), 'image_07704.jpg': array(96, dtype=int64), 'image_07705.jpg': array(96, dtype=int64), 'image_07706.jpg': array(96, dtype=int64), 'image_07707.jpg': array(96, dtype=int64), 'image_07710.jpg': array(96, dtype=int64), 'image_07711.jpg': array(96, dtype=int64), 'image_07712.jpg': array(96, dtype=int64), 'image_07713.jpg': array(96, dtype=int64), 'image_07714.jpg': array(96, dtype=int64), 'image_07715.jpg': array(96, dtype=int64), 'image_07716.jpg': array(96, dtype=int64), 'image_07717.jpg': array(96, dtype=int64), 'image_07720.jpg': array(96, dtype=int64), 'image_07721.jpg': array(96, dtype=int64), 'image_07722.jpg': array(96, dtype=int64), 'image_07724.jpg': array(96, dtype=int64), 'image_07725.jpg': array(96, dtype=int64), 'image_07726.jpg': array(96, dtype=int64), 'image_07728.jpg': array(96, dtype=int64), 'image_07729.jpg': array(96, dtype=int64), 'image_07730.jpg': array(96, dtype=int64), 'image_07731.jpg': array(96, dtype=int64), 'image_07732.jpg': array(96, dtype=int64), 'image_07733.jpg': array(96, dtype=int64), 'image_07734.jpg': array(96, dtype=int64), 'image_07735.jpg': array(96, dtype=int64), 'image_07736.jpg': array(96, dtype=int64), 'image_07738.jpg': array(96, dtype=int64), 'image_07740.jpg': array(96, dtype=int64), 'image_07741.jpg': array(96, dtype=int64), 'image_07742.jpg': array(96, dtype=int64), 'image_07743.jpg': array(96, dtype=int64), 'image_07744.jpg': array(96, dtype=int64), 'image_07746.jpg': array(96, dtype=int64), 'image_07747.jpg': array(96, dtype=int64), 'image_07748.jpg': array(96, dtype=int64), 'image_07749.jpg': array(96, dtype=int64), 'image_07750.jpg': array(96, dtype=int64), 'image_07751.jpg': array(97, dtype=int64), 'image_07752.jpg': array(97, dtype=int64), 'image_07754.jpg': array(97, dtype=int64), 'image_07755.jpg': array(97, dtype=int64), 'image_07756.jpg': array(97, dtype=int64), 'image_07759.jpg': array(97, dtype=int64), 'image_07760.jpg': array(97, dtype=int64), 'image_07761.jpg': array(97, dtype=int64), 'image_07762.jpg': array(97, dtype=int64), 'image_07763.jpg': array(97, dtype=int64), 'image_07765.jpg': array(97, dtype=int64), 'image_07767.jpg': array(97, dtype=int64), 'image_07768.jpg': array(97, dtype=int64), 'image_07770.jpg': array(97, dtype=int64), 'image_07771.jpg': array(97, dtype=int64), 'image_07772.jpg': array(97, dtype=int64), 'image_07773.jpg': array(97, dtype=int64), 'image_07774.jpg': array(97, dtype=int64), 'image_07775.jpg': array(97, dtype=int64), 'image_07776.jpg': array(97, dtype=int64), 'image_07779.jpg': array(97, dtype=int64), 'image_07780.jpg': array(97, dtype=int64), 'image_07781.jpg': array(97, dtype=int64), 'image_07782.jpg': array(97, dtype=int64), 'image_07783.jpg': array(97, dtype=int64), 'image_07784.jpg': array(97, dtype=int64), 'image_07785.jpg': array(97, dtype=int64), 'image_07786.jpg': array(97, dtype=int64), 'image_07788.jpg': array(97, dtype=int64), 'image_07789.jpg': array(97, dtype=int64), 'image_07790.jpg': array(97, dtype=int64), 'image_07791.jpg': array(97, dtype=int64), 'image_07793.jpg': array(97, dtype=int64), 'image_07794.jpg': array(97, dtype=int64), 'image_07795.jpg': array(97, dtype=int64), 'image_07797.jpg': array(97, dtype=int64), 'image_07798.jpg': array(97, dtype=int64), 'image_07799.jpg': array(97, dtype=int64), 'image_07800.jpg': array(97, dtype=int64), 'image_07801.jpg': array(97, dtype=int64), 'image_07802.jpg': array(97, dtype=int64), 'image_07803.jpg': array(97, dtype=int64), 'image_07804.jpg': array(97, dtype=int64), 'image_07805.jpg': array(97, dtype=int64), 'image_07806.jpg': array(97, dtype=int64), 'image_07807.jpg': array(97, dtype=int64), 'image_07808.jpg': array(97, dtype=int64), 'image_07809.jpg': array(97, dtype=int64), 'image_07811.jpg': array(97, dtype=int64), 'image_07812.jpg': array(97, dtype=int64), 'image_07813.jpg': array(97, dtype=int64), 'image_07814.jpg': array(97, dtype=int64), 'image_07815.jpg': array(97, dtype=int64), 'image_07816.jpg': array(97, dtype=int64), 'image_07817.jpg': array(97, dtype=int64), 'image_07818.jpg': array(97, dtype=int64), 'image_07819.jpg': array(97, dtype=int64), 'image_07821.jpg': array(97, dtype=int64), 'image_07822.jpg': array(97, dtype=int64), 'image_07823.jpg': array(97, dtype=int64), 'image_07824.jpg': array(97, dtype=int64), 'image_07825.jpg': array(97, dtype=int64), 'image_07826.jpg': array(97, dtype=int64), 'image_07828.jpg': array(97, dtype=int64), 'image_07829.jpg': array(97, dtype=int64), 'image_07830.jpg': array(97, dtype=int64), 'image_07831.jpg': array(97, dtype=int64), 'image_07832.jpg': array(97, dtype=int64), 'image_07834.jpg': array(98, dtype=int64), 'image_07835.jpg': array(98, dtype=int64), 'image_07836.jpg': array(98, dtype=int64), 'image_07837.jpg': array(98, dtype=int64), 'image_07839.jpg': array(98, dtype=int64), 'image_07841.jpg': array(98, dtype=int64), 'image_07842.jpg': array(98, dtype=int64), 'image_07843.jpg': array(98, dtype=int64), 'image_07844.jpg': array(98, dtype=int64), 'image_07845.jpg': array(98, dtype=int64), 'image_07846.jpg': array(98, dtype=int64), 'image_07847.jpg': array(98, dtype=int64), 'image_07848.jpg': array(98, dtype=int64), 'image_07849.jpg': array(98, dtype=int64), 'image_07850.jpg': array(98, dtype=int64), 'image_07851.jpg': array(98, dtype=int64), 'image_07852.jpg': array(98, dtype=int64), 'image_07853.jpg': array(98, dtype=int64), 'image_07855.jpg': array(98, dtype=int64), 'image_07856.jpg': array(98, dtype=int64), 'image_07858.jpg': array(98, dtype=int64), 'image_07859.jpg': array(98, dtype=int64), 'image_07861.jpg': array(98, dtype=int64), 'image_07863.jpg': array(98, dtype=int64), 'image_07864.jpg': array(98, dtype=int64), 'image_07865.jpg': array(98, dtype=int64), 'image_07866.jpg': array(98, dtype=int64), 'image_07867.jpg': array(98, dtype=int64), 'image_07868.jpg': array(98, dtype=int64), 'image_07870.jpg': array(98, dtype=int64), 'image_07872.jpg': array(98, dtype=int64), 'image_07876.jpg': array(98, dtype=int64), 'image_07877.jpg': array(98, dtype=int64), 'image_07878.jpg': array(98, dtype=int64), 'image_07879.jpg': array(98, dtype=int64), 'image_07880.jpg': array(98, dtype=int64), 'image_07881.jpg': array(98, dtype=int64), 'image_07882.jpg': array(98, dtype=int64), 'image_07883.jpg': array(98, dtype=int64), 'image_07884.jpg': array(98, dtype=int64), 'image_07885.jpg': array(98, dtype=int64), 'image_07886.jpg': array(98, dtype=int64), 'image_07887.jpg': array(98, dtype=int64), 'image_07888.jpg': array(98, dtype=int64), 'image_07889.jpg': array(98, dtype=int64), 'image_07890.jpg': array(98, dtype=int64), 'image_07891.jpg': array(98, dtype=int64), 'image_07892.jpg': array(98, dtype=int64), 'image_08062.jpg': array(98, dtype=int64), 'image_08064.jpg': array(98, dtype=int64)}\n"
     ]
    }
   ],
   "source": [
    "print(load_annotations('./flower_data/train.txt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 任务2：分别把数据和标签都存在list里\n",
    "\n",
    "- 不是我非让你存list里，因为dataloader到时候会在这里取数据\n",
    "- 按照人家要求来，不要耍个性，让整list咱就给人家整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-04-11T16:07:34.718149300Z",
     "start_time": "2024-04-11T16:07:34.673600300Z"
    }
   },
   "outputs": [],
   "source": [
    "img_label = load_annotations('./flower_data/train.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-04-11T16:07:34.718149300Z",
     "start_time": "2024-04-11T16:07:34.682031700Z"
    }
   },
   "outputs": [],
   "source": [
    "image_name = list(img_label.keys())\n",
    "label = list(img_label.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T16:07:34.735970800Z",
     "start_time": "2024-04-11T16:07:34.687531900Z"
    }
   },
   "outputs": [
    {
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     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T16:07:34.739993400Z",
     "start_time": "2024-04-11T16:07:34.700149800Z"
    }
   },
   "outputs": [
    {
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     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 任务3：图像数据路径得完整\n",
    "\n",
    "- 因为一会咱得用这个路径去读数据，所以路径得加上前缀\n",
    "- 以后大家任务不同，数据不同，怎么加你看着来就行，反正得能读到图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-04-11T16:07:34.741008500Z",
     "start_time": "2024-04-11T16:07:34.731462800Z"
    }
   },
   "outputs": [],
   "source": [
    "data_dir = './flower_data/'\n",
    "train_dir = data_dir + '/train_filelist'\n",
    "valid_dir = data_dir + '/val_filelist'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T16:07:34.821807800Z",
     "start_time": "2024-04-11T16:07:34.738974600Z"
    }
   },
   "outputs": [
    {
     "data": {
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'./flower_data//train_filelist\\\\image_06522.jpg',\n './flower_data//train_filelist\\\\image_06523.jpg',\n './flower_data//train_filelist\\\\image_06524.jpg',\n './flower_data//train_filelist\\\\image_06525.jpg',\n './flower_data//train_filelist\\\\image_06527.jpg',\n './flower_data//train_filelist\\\\image_06850.jpg',\n './flower_data//train_filelist\\\\image_06851.jpg',\n './flower_data//train_filelist\\\\image_06852.jpg',\n './flower_data//train_filelist\\\\image_06853.jpg',\n './flower_data//train_filelist\\\\image_06854.jpg',\n ...]"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_path = [os.path.join(train_dir, img) for img in image_name]\n",
    "image_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 任务4：把上面那几个事得写在一起\n",
    "\n",
    "- 1.注意要使用from torch.utils.data import Dataset, DataLoader\n",
    "- 2.类名定义class FlowerDataset(Dataset)，其中FlowerDataset可以改成自己的名字\n",
    "- 3.def __init__(self, root_dir, ann_file, transform=None):咱们要根据自己任务重写\n",
    "- 4.def __getitem__(self, idx):根据自己任务，返回图像数据和标签数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-04-11T16:07:34.822807600Z",
     "start_time": "2024-04-11T16:07:34.764091400Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class FlowerDataset(Dataset):\n",
    "    def __init__(self, root_dir, ann_file, transform=None):\n",
    "        self.ann_file = ann_file  # 标签文本文件路径\n",
    "        self.root_dir = root_dir  # 根目录路径\n",
    "        self.img_label = self.load_annotations()  # 获取图片名称与标签对应关系\n",
    "        self.img = [os.path.join(self.root_dir, img) for img in list(self.img_label.keys())]  # 图片文件的绝对路径列表\n",
    "        self.label = [label for label in list(self.img_label.values())]  # 图片文件对应的标签列表 \n",
    "        self.transform = transform  # 预处理\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.img)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        image = Image.open(self.img[idx])\n",
    "        label = self.label[idx]\n",
    "        if self.transform:  # 有预处理就进行预处理\n",
    "            image = self.transform(image)\n",
    "        label = torch.from_numpy(np.array(label))  # 转为张量类型\n",
    "        return image, label\n",
    "\n",
    "    def load_annotations(self):\n",
    "        data_infos = {}\n",
    "        with open(self.ann_file) as f:\n",
    "            samples = [x.strip().split(' ') for x in f.readlines()]\n",
    "            for filename, gt_label in samples:\n",
    "                data_infos[filename] = np.array(gt_label, dtype=np.int64)\n",
    "        return data_infos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 任务5：数据预处理(transform)\n",
    "\n",
    "- 1.预处理的事都在上面的__getitem__中完成，需要对图像和标签咋咋地的，要整啥事，都在上面整\n",
    "- 2.返回的数据和标签就是建模时模型的输入和损失函数中标签的输入，一定整明白自己模型要啥\n",
    "- 3.预处理这个事是你定的，不同的数据需要的方法也不一样，下面给出的是比较通用的方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-04-11T16:07:34.822807600Z",
     "start_time": "2024-04-11T16:07:34.768505900Z"
    }
   },
   "outputs": [],
   "source": [
    "data_transforms = {\n",
    "    'train':\n",
    "        transforms.Compose([\n",
    "            transforms.Resize(64),\n",
    "            transforms.RandomRotation(45),  # 随机旋转，-45到45度之间随机选\n",
    "            transforms.CenterCrop(64),  # 从中心开始裁剪\n",
    "            transforms.RandomHorizontalFlip(p=0.5),  # 随机水平翻转 选择一个概率概率\n",
    "            transforms.RandomVerticalFlip(p=0.5),  # 随机垂直翻转\n",
    "            transforms.ColorJitter(brightness=0.2, contrast=0.1, saturation=0.1, hue=0.1),\n",
    "            # 参数1为亮度，参数2为对比度，参数3为饱和度，参数4为色相\n",
    "            transforms.RandomGrayscale(p=0.025),  # 概率转换成灰度率，3通道就是R=G=B\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])  # 均值，标准差\n",
    "        ]),\n",
    "    'valid':\n",
    "        transforms.Compose([\n",
    "            transforms.Resize(64),\n",
    "            transforms.CenterCrop(64),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "        ]),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 任务6：根据写好的class FlowerDataset(Dataset):来实例化咱们的dataloader\n",
    "\n",
    "- 1.构建数据集：分别创建训练和验证用的数据集（如果需要测试集也一样的方法）\n",
    "- 2.用Torch给的DataLoader方法来实例化(batch啥的自己定，根据你的显存来选合适的)\n",
    "- 3.打印看看数据里面是不是有东西了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-04-11T16:07:34.822807600Z",
     "start_time": "2024-04-11T16:07:34.775335900Z"
    }
   },
   "outputs": [],
   "source": [
    "# 生成训练集和验证集的dataset\n",
    "train_dataset = FlowerDataset(root_dir=train_dir, ann_file='./flower_data/train.txt', transform=data_transforms['train'])\n",
    "val_dataset = FlowerDataset(root_dir=valid_dir, ann_file='./flower_data/val.txt', transform=data_transforms['valid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-04-11T16:07:34.823807700Z",
     "start_time": "2024-04-11T16:07:34.799234900Z"
    }
   },
   "outputs": [],
   "source": [
    "# 生成训练集和验证集的DataLoader\n",
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=64, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T16:07:34.823807700Z",
     "start_time": "2024-04-11T16:07:34.803826700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "6552"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T16:07:34.823807700Z",
     "start_time": "2024-04-11T16:07:34.810391400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "818"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(val_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 任务7：用之前先试试，整个数据和标签对应下，看看对不对\n",
    "\n",
    "- 1.别着急往模型里传，对不对都不知道呢\n",
    "- 2.用这个方法：iter(train_loader).next()来试试，得到的数据和标签是啥\n",
    "- 3.看不出来就把图画出来，标签打印出来，确保自己整的数据集没啥问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T16:09:20.441726Z",
     "start_time": "2024-04-11T16:09:19.701374200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label is: 73\n"
     ]
    }
   ],
   "source": [
    "# 查看训练集的一个图片\n",
    "image, label = next(iter(train_loader))     # 迭代获取1个batch的数据\n",
    "# print(image.shape)                        # 输出[64, 3, 64, 64] 表示[当前batch包含的图像数量, 通道数, 高度, 宽度]\n",
    "sample = image[0].squeeze()                 # 获取其中第一个图像数据，压缩其维度，变为 [3, 64, 64]\n",
    "sample = sample.permute((1, 2, 0)).numpy()  # 变换维度并且转为numpy格式，将[3, 64, 64]转为[64, 64, 3]\n",
    "# 由于在预处理时对图像数据进行了标准化操作，故这里要对图像数据进行还原，乘上标准差后加上均值\n",
    "sample = sample * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406))\n",
    "# sample = np.clip(sample, 0, 1)  # 如果像素值是浮点数\n",
    "# sample = np.clip(sample, 0, 255)  # 如果像素值是整数\n",
    "plt.imshow(sample)\n",
    "plt.show()\n",
    "print('Label is: {}'.format(label[0].numpy()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T16:09:40.315182700Z",
     "start_time": "2024-04-11T16:09:39.571278900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label is: 96\n"
     ]
    }
   ],
   "source": [
    "# 查看验证集的一个图片\n",
    "image, label = next(iter(val_loader))     # 迭代获取1个batch的数据\n",
    "sample = image[0].squeeze()\n",
    "sample = sample.permute((1, 2, 0)).numpy()\n",
    "sample *= [0.229, 0.224, 0.225]\n",
    "sample += [0.485, 0.456, 0.406]\n",
    "plt.imshow(sample)\n",
    "plt.show()\n",
    "print('Label is: {}'.format(label[0].numpy()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 任务8：咋用就是你来定了，把模型啥的整好往里面传吧\n",
    "\n",
    "- 下面这些事之前都唠过了，按照自己习惯的方法整就得了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-04-11T16:10:07.051340100Z",
     "start_time": "2024-04-11T16:10:07.037784800Z"
    }
   },
   "outputs": [],
   "source": [
    "dataloaders = {'train': train_loader, 'valid': val_loader}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-04-11T16:10:10.271620600Z",
     "start_time": "2024-04-11T16:10:10.270071Z"
    }
   },
   "outputs": [],
   "source": [
    "model_name = 'resnet'  #可选的比较多 ['resnet', 'alexnet', 'vgg', 'squeezenet', 'densenet', 'inception']\n",
    "#是否用人家训练好的特征来做\n",
    "feature_extract = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T16:10:11.682447600Z",
     "start_time": "2024-04-11T16:10:11.628548Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA is available!  Training on GPU ...\n"
     ]
    }
   ],
   "source": [
    "# 是否用GPU训练\n",
    "train_on_gpu = torch.cuda.is_available()\n",
    "\n",
    "if not train_on_gpu:\n",
    "    print('CUDA is not available.  Training on CPU ...')\n",
    "else:\n",
    "    print('CUDA is available!  Training on GPU ...')\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T16:10:13.320565500Z",
     "start_time": "2024-04-11T16:10:13.224997100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n  (layer1): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n  (fc): Linear(in_features=512, out_features=1000, bias=True)\n)"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ft = models.resnet18()\n",
    "model_ft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T16:10:14.897987400Z",
     "start_time": "2024-04-11T16:10:14.885789100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n  (layer1): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n  (fc): Sequential(\n    (0): Linear(in_features=512, out_features=102, bias=True)\n  )\n)"
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_ftrs = model_ft.fc.in_features\n",
    "model_ft.fc = nn.Sequential(nn.Linear(num_ftrs, 102))\n",
    "input_size = 64\n",
    "model_ft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-04-11T16:10:15.930633200Z",
     "start_time": "2024-04-11T16:10:15.923993300Z"
    }
   },
   "outputs": [],
   "source": [
    "# 优化器设置\n",
    "optimizer_ft = optim.Adam(model_ft.parameters(), lr=1e-3)\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)  #学习率每7个epoch衰减成原来的1/10\n",
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-04-11T16:10:17.069960200Z",
     "start_time": "2024-04-11T16:10:17.050762Z"
    }
   },
   "outputs": [],
   "source": [
    "def train_model(model, dataloaders, criterion, optimizer, num_epochs=25, is_inception=False, filename='best.pth'):\n",
    "    since = time.time()\n",
    "    best_acc = 0\n",
    "    model.to(device)\n",
    "\n",
    "    val_acc_history = []\n",
    "    train_acc_history = []\n",
    "    train_losses = []\n",
    "    valid_losses = []\n",
    "    LRs = [optimizer.param_groups[0]['lr']]\n",
    "\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "        print('-' * 10)\n",
    "\n",
    "        # 训练和验证\n",
    "        for phase in ['train', 'valid']:\n",
    "            if phase == 'train':\n",
    "                model.train()  # 训练\n",
    "            else:\n",
    "                model.eval()  # 验证\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "\n",
    "            # 把数据都取个遍\n",
    "            for inputs, labels in dataloaders[phase]:\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                # 清零\n",
    "                optimizer.zero_grad()\n",
    "                # 只有训练的时候计算和更新梯度\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    outputs = model(inputs)\n",
    "                    loss = criterion(outputs, labels)\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "                    #print(loss)\n",
    "\n",
    "                    # 训练阶段更新权重\n",
    "                    if phase == 'train':\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "\n",
    "                # 计算损失\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "\n",
    "            epoch_loss = running_loss / len(dataloaders[phase].dataset)\n",
    "            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)\n",
    "\n",
    "            time_elapsed = time.time() - since\n",
    "            print('Time elapsed {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\n",
    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))\n",
    "\n",
    "            # 得到最好那次的模型\n",
    "            if phase == 'valid' and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "                state = {\n",
    "                    'state_dict': model.state_dict(),  #字典里key就是各层的名字，值就是训练好的权重\n",
    "                    'best_acc': best_acc,\n",
    "                    'optimizer': optimizer.state_dict(),  #优化器的状态信息\n",
    "                }\n",
    "                torch.save(state, filename)\n",
    "            if phase == 'valid':\n",
    "                val_acc_history.append(epoch_acc)\n",
    "                valid_losses.append(epoch_loss)\n",
    "                scheduler.step(epoch_loss)  #学习率衰减\n",
    "            if phase == 'train':\n",
    "                train_acc_history.append(epoch_acc)\n",
    "                train_losses.append(epoch_loss)\n",
    "\n",
    "        print('Optimizer learning rate : {:.7f}'.format(optimizer.param_groups[0]['lr']))\n",
    "        LRs.append(optimizer.param_groups[0]['lr'])\n",
    "        print()\n",
    "\n",
    "    time_elapsed = time.time() - since\n",
    "    print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\n",
    "    print('Best val Acc: {:4f}'.format(best_acc))\n",
    "\n",
    "    # 训练完后用最好的一次当做模型最终的结果,等着一会测试\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model, val_acc_history, train_acc_history, valid_losses, train_losses, LRs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-11T16:22:30.483286100Z",
     "start_time": "2024-04-11T16:10:18.840551600Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/19\n",
      "----------\n",
      "Time elapsed 1m 13s\n",
      "train Loss: 4.0006 Acc: 0.0830\n",
      "Time elapsed 1m 19s\n",
      "valid Loss: 4.0089 Acc: 0.1076\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 1/19\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\anaconda3\\envs\\gupao\\Lib\\site-packages\\torch\\optim\\lr_scheduler.py:156: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.\n",
      "  warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed 1m 51s\n",
      "train Loss: 3.4443 Acc: 0.1549\n",
      "Time elapsed 1m 54s\n",
      "valid Loss: 3.2724 Acc: 0.1773\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 2/19\n",
      "----------\n",
      "Time elapsed 2m 25s\n",
      "train Loss: 3.1216 Acc: 0.2138\n",
      "Time elapsed 2m 28s\n",
      "valid Loss: 3.0282 Acc: 0.2262\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 3/19\n",
      "----------\n",
      "Time elapsed 2m 59s\n",
      "train Loss: 2.8769 Acc: 0.2590\n",
      "Time elapsed 3m 2s\n",
      "valid Loss: 2.6903 Acc: 0.2983\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 4/19\n",
      "----------\n",
      "Time elapsed 3m 35s\n",
      "train Loss: 2.7065 Acc: 0.2952\n",
      "Time elapsed 3m 38s\n",
      "valid Loss: 2.5794 Acc: 0.3154\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 5/19\n",
      "----------\n",
      "Time elapsed 4m 9s\n",
      "train Loss: 2.4852 Acc: 0.3465\n",
      "Time elapsed 4m 12s\n",
      "valid Loss: 2.3033 Acc: 0.4083\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 6/19\n",
      "----------\n",
      "Time elapsed 4m 44s\n",
      "train Loss: 2.2922 Acc: 0.3887\n",
      "Time elapsed 4m 46s\n",
      "valid Loss: 2.3985 Acc: 0.3704\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 7/19\n",
      "----------\n",
      "Time elapsed 5m 18s\n",
      "train Loss: 2.1769 Acc: 0.4132\n",
      "Time elapsed 5m 21s\n",
      "valid Loss: 2.5718 Acc: 0.3631\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 8/19\n",
      "----------\n",
      "Time elapsed 5m 53s\n",
      "train Loss: 1.9950 Acc: 0.4637\n",
      "Time elapsed 5m 56s\n",
      "valid Loss: 2.0258 Acc: 0.4707\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 9/19\n",
      "----------\n",
      "Time elapsed 6m 29s\n",
      "train Loss: 1.8688 Acc: 0.4915\n",
      "Time elapsed 6m 32s\n",
      "valid Loss: 1.8887 Acc: 0.4939\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 10/19\n",
      "----------\n",
      "Time elapsed 7m 3s\n",
      "train Loss: 1.7648 Acc: 0.5116\n",
      "Time elapsed 7m 7s\n",
      "valid Loss: 1.8335 Acc: 0.5073\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 11/19\n",
      "----------\n",
      "Time elapsed 7m 38s\n",
      "train Loss: 1.6532 Acc: 0.5385\n",
      "Time elapsed 7m 41s\n",
      "valid Loss: 1.8607 Acc: 0.5293\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 12/19\n",
      "----------\n",
      "Time elapsed 8m 12s\n",
      "train Loss: 1.5278 Acc: 0.5771\n",
      "Time elapsed 8m 15s\n",
      "valid Loss: 1.6607 Acc: 0.5562\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 13/19\n",
      "----------\n",
      "Time elapsed 8m 47s\n",
      "train Loss: 1.4547 Acc: 0.5905\n",
      "Time elapsed 8m 49s\n",
      "valid Loss: 1.8748 Acc: 0.5208\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 14/19\n",
      "----------\n",
      "Time elapsed 9m 20s\n",
      "train Loss: 1.3861 Acc: 0.6090\n",
      "Time elapsed 9m 23s\n",
      "valid Loss: 1.5064 Acc: 0.6161\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 15/19\n",
      "----------\n",
      "Time elapsed 9m 54s\n",
      "train Loss: 1.3000 Acc: 0.6348\n",
      "Time elapsed 9m 57s\n",
      "valid Loss: 1.4444 Acc: 0.6112\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 16/19\n",
      "----------\n",
      "Time elapsed 10m 27s\n",
      "train Loss: 1.2203 Acc: 0.6441\n",
      "Time elapsed 10m 30s\n",
      "valid Loss: 1.2892 Acc: 0.6443\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 17/19\n",
      "----------\n",
      "Time elapsed 11m 2s\n",
      "train Loss: 1.1620 Acc: 0.6662\n",
      "Time elapsed 11m 5s\n",
      "valid Loss: 1.5979 Acc: 0.5795\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 18/19\n",
      "----------\n",
      "Time elapsed 11m 35s\n",
      "train Loss: 1.1119 Acc: 0.6787\n",
      "Time elapsed 11m 38s\n",
      "valid Loss: 1.4296 Acc: 0.6320\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Epoch 19/19\n",
      "----------\n",
      "Time elapsed 12m 8s\n",
      "train Loss: 1.0318 Acc: 0.6996\n",
      "Time elapsed 12m 12s\n",
      "valid Loss: 1.2245 Acc: 0.6663\n",
      "Optimizer learning rate : 0.0010000\n",
      "\n",
      "Training complete in 12m 12s\n",
      "Best val Acc: 0.666259\n"
     ]
    }
   ],
   "source": [
    "model_ft, val_acc_history, train_acc_history, valid_losses, train_losses, LRs = train_model(model_ft, dataloaders,\n",
    "                                                                                            criterion, optimizer_ft,\n",
    "                                                                                            num_epochs=20,\n",
    "                                                                                            filename='best.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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